ontological-renamer
Renames projects and content with dense, meaningful ontological titles that describe essence and function. Combines 3-4 words using separator conventions (- for compound/close words, -- for distant concepts). Provides translations to Latin and Greek. Use when naming projects, repositories, systems, or concepts.
Best use case
ontological-renamer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Renames projects and content with dense, meaningful ontological titles that describe essence and function. Combines 3-4 words using separator conventions (- for compound/close words, -- for distant concepts). Provides translations to Latin and Greek. Use when naming projects, repositories, systems, or concepts.
Teams using ontological-renamer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ontological-renamer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ontological-renamer Compares
| Feature / Agent | ontological-renamer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Renames projects and content with dense, meaningful ontological titles that describe essence and function. Combines 3-4 words using separator conventions (- for compound/close words, -- for distant concepts). Provides translations to Latin and Greek. Use when naming projects, repositories, systems, or concepts.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Ontological Renamer
Create dense, meaningful names for projects, systems, and concepts using ontological principles and classical language translations.
## When to Use
- Naming new projects or repositories
- Renaming existing systems
- Creating brand identities
- Naming concepts, patterns, or frameworks
- Building taxonomies and vocabularies
## Naming Methodology
### Step 1: Identify Essence
What IS the thing? (noun/subject)
```
Questions:
- What category does it belong to?
- What is its fundamental nature?
- What metaphor best describes it?
Examples:
- A collection → repository, vault, archive
- A process → engine, forge, factory
- A connection → bridge, link, weave
```
### Step 2: Identify Function
What does it DO? (verb/action)
```
Questions:
- What transformation does it perform?
- What problem does it solve?
- What capability does it provide?
Examples:
- Creates → forge, builder, generator
- Connects → bridge, link, mesh
- Organizes → lattice, matrix, graph
```
### Step 3: Identify Domain
What CONTEXT does it operate in?
```
Questions:
- What field or industry?
- What system or ecosystem?
- What users or actors?
Examples:
- AI/ML → agent, model, neural
- Development → code, build, deploy
- Knowledge → wisdom, expertise, craft
```
### Step 4: Combine
Form a 3-4 word compound that reads like a dense sentence.
```
Pattern: [Domain]-[Function]--[Essence]-[Modifier]
Examples:
- agent-skill--knowledge-forge
- code-craft--pattern-vault
- data-weave--insight-engine
```
## Separator Conventions
### Single Hyphen (-)
Use for magnetically close concepts that form a compound:
```
skill-bundle → "bundle of skills" (compound noun)
agent-powered → "powered by agents" (adjective)
code-craft → "craft of coding" (compound noun)
knowledge-base → "base of knowledge" (compound noun)
```
### Double Hyphen (--)
Use for conceptual distance or category separation:
```
agent-skills--knowledge-engine
├── agent-skills (what it contains)
└── knowledge-engine (what it is)
frontend-toolkit--design-system
├── frontend-toolkit (domain)
└── design-system (function)
```
### Decision Matrix
| Relationship | Separator | Example |
|--------------|-----------|---------|
| Adjective + Noun | - | `smart-agent` |
| Noun + Noun (compound) | - | `skill-bundle` |
| Category + Instance | -- | `dev-tools--linter` |
| Domain + Function | -- | `ml-ops--model-registry` |
| What + How | -- | `data--transform-engine` |
## Word Selection Rules
### Ignore These Words
When forming names, filter out:
```
Articles: a, an, the
Prepositions: of, for, to, in, on, by, with
Conjunctions: and, or, but
```
### Prefer Strong Words
```
Strong Nouns:
✓ forge, vault, engine, lattice, codex
✗ thing, stuff, helper, manager
Active Verbs:
✓ forge, weave, craft, build
✗ do, make, handle, process
Specific Terms:
✓ authentication, pipeline, schema
✗ security, flow, structure
```
### Domain-Specific Vocabulary
| Domain | Strong Words |
|--------|--------------|
| AI/Agents | agent, model, neural, inference, swarm |
| Development | code, build, deploy, pipeline, stack |
| Data | schema, query, transform, pipeline |
| Security | vault, shield, guard, sentinel |
| Knowledge | codex, archive, wisdom, expertise |
## Classical Translations
### Translation Approach
1. Identify root concepts in English
2. Find Latin/Greek equivalents
3. Combine following classical morphology
4. Verify pronunciation and semantic accuracy
### Common Roots
| English | Latin | Greek |
|---------|-------|-------|
| skill | ars, artis | technē |
| knowledge | scientia | epistēmē, gnōsis |
| agent | agens, agentis | praktōr |
| forge | fabrica | chalkeion |
| vault | camera | thalamos |
| codex | codex | biblos |
| mastery | magisterium | epistasia |
| bundle | fasciculus | desmos |
| craft | ars | technē |
| power | potestas | dynamis |
### Translation Examples
```
skill-codex--agent-mastery
├── Latin: artium-codex--agentis-magisterium
└── Greek: technōn-biblos--praktoros-epistasia
agent-skill--knowledge-forge
├── Latin: ars-agentis--scientia-fabrica
└── Greek: technē-praktor--epistēmē-chalkeion
```
## Name Generation Process
### Example: AI Skills Repository
**Context:** A repository of skills that extend AI agent capabilities.
**Step 1: Essence**
- It's a collection (repository, vault, archive)
- It contains skills (capabilities, expertise)
- It's curated (codex, library)
**Step 2: Function**
- Extends capabilities (augment, enhance)
- Provides knowledge (expertise, mastery)
- Bundles together (bundle, collection)
**Step 3: Domain**
- AI agents (agent, autonomous)
- Skills/capabilities (skill, craft, art)
- Knowledge systems (knowledge, wisdom)
**Step 4: Generate Candidates**
```
1. agent-skill--knowledge-forge
"A forge of knowledge for agent skills"
2. skill-codex--agent-mastery
"A codex of skills for agent mastery"
3. capability-weave--agent-loom
"A loom that weaves agent capabilities"
4. praxis-bundle--agent-expertise
"A bundle of practical expertise for agents"
5. craft-matrix--agent-powers
"A matrix of crafts that power agents"
```
**Step 5: Evaluate**
| Name | Clarity | Memorability | Domain Fit |
|------|---------|--------------|------------|
| skill-codex--agent-mastery | High | High | High |
| agent-skill--knowledge-forge | High | Medium | High |
| capability-weave--agent-loom | Medium | Medium | Medium |
**Winner:** `skill-codex--agent-mastery`
### Example: Authentication Library
**Context:** A library for handling user authentication.
**Candidates:**
```
1. auth-guard--access-sentinel
"A sentinel guarding access through authentication"
2. identity-forge--credential-vault
"A vault of credentials from an identity forge"
3. login-shield--session-keeper
"A shield and keeper of login sessions"
```
## Output Format
When generating names, provide:
```yaml
naming_proposal:
context: "Brief description of what's being named"
candidates:
- name: "english-compound--name"
meaning: "What this name conveys"
latin: "latin-translation"
greek: "greek-translation"
pros: ["list", "of", "strengths"]
cons: ["list", "of", "weaknesses"]
recommendation:
name: "recommended-name"
rationale: "Why this is the best choice"
```
## Anti-Patterns
### Avoid These
```
❌ Generic Names
my-project, cool-tool, awesome-thing
❌ Overloaded Terms
manager, handler, service, util
❌ Acronyms Without Meaning
XYZPT, QWERT
❌ Too Long
super-advanced-machine-learning-based-intelligent-agent-system
❌ Unclear Separators
agent_skill-knowledge--forge (mixing _ and -)
```
### Prefer These
```
✓ Evocative Metaphors
forge, vault, weave, lattice
✓ Domain-Specific Terms
codex, praxis, schema
✓ Clear Compound Structure
skill-bundle, knowledge-forge
✓ Balanced Length (2-5 words)
agent-skill--knowledge-forge
```
## References
- `references/naming-patterns.md` — Compound naming patterns
- `references/separator-conventions.md` — Detailed separator rules
- `references/translation-guide.md` — Latin/Greek translation guidance
- `references/workflow-integration.md` — Ecosystem integration
- `assets/word-roots.md` — Common Latin/Greek roots
## Related Skills
- **documentation-generator**: For documenting named concepts
- **brand-guidelines**: For visual identity to match namesRelated Skills
taxonomy-modeling-design
Phase 2 of the pentaphase structural-overhaul protocol. Classifies entities, standardizes attributes, establishes relationships, and designs the access framework. Use when the user invokes phase 2 of an overhaul, asks to "design the taxonomy" or "model the structure", or has completed a landscape audit and is ready to redesign. Consumes phase-1-landscape-report.md; produces phase-2-taxonomy-model.md.
systemic-ingestion-normalization
Phase 4 of the pentaphase structural-overhaul protocol. Purges redundancies, enriches and aligns legacy entities to the new schema, executes phased ingestion into the new environment, and audits integrity. Use when the user invokes phase 4 of an overhaul, asks to "migrate the data" or "ingest into the new system", or has a configured environment ready to accept legacy entities. Consumes phase-3-environment-spec.md; produces phase-4-ingestion-report.md.
system-environment-configuration
Phase 3 of the pentaphase structural-overhaul protocol. Translates the taxonomy model into objective technical criteria, evaluates candidate mechanisms or frameworks, instantiates the chosen architecture, and programs validation rules. Use when the user invokes phase 3 of an overhaul, asks to "select a system" or "configure the environment", or has a taxonomy model and is ready to choose technology. Consumes phase-2-taxonomy-model.md; produces phase-3-environment-spec.md.
pentaphase-orchestrator
Threads the full five-phase structural-overhaul protocol — landscape discovery, taxonomy design, environment configuration, systemic ingestion, governance evolution — for any substrate the user names. Use when the user requests a structural overhaul, system redesign, or end-to-end restructuring of a documentation system, asset registry, code monorepo, knowledge base, or operational workflow; or when they explicitly invoke the pentaphase methodology. Coordinates handoffs between phase-skills and seats validation gates between phases.
landscape-discovery-audit
Phase 1 of the pentaphase structural-overhaul protocol. Inventories assets, maps current flow, identifies friction, and defines value metrics for any substrate. Use when the user invokes phase 1 of an overhaul, requests a baseline audit, asks to "discover the landscape" of a system, or wants to understand current state before redesigning. Produces phase-1-landscape-report.md.
governance-evolution-protocol
Phase 5 of the pentaphase structural-overhaul protocol. Codifies operational protocols, onboards the ecosystem of participants, programs behavior monitoring, and establishes an iteration cadence so the substrate evolves rather than calcifies. Use when the user invokes phase 5 of an overhaul, asks to "establish governance" or "lock in the protocols", or has completed ingestion and is ready to declare the substrate operational. Consumes phase-4-ingestion-report.md; produces phase-5-governance-charter.md, which closes the protocol.
dimension-surfacing
Surfaces the parallel domain dimensions implicit in a dense or minimal prompt. Use when a user prompt is small on the surface but plainly implies multiple independent domains needing different expertise; when explicitly invoked by the coliseum-orchestrator skill as Phase 1; or when the user asks "what dimensions does this prompt encode" or "what axes does this break into." Produces a named dimension set where each dimension is independently executable and not a paraphrase of another.
coliseum-dispatch
Dispatches a composed set of assignment envelopes to domain-expert subagents in parallel, in a single message with multiple Agent tool calls. Enforces the no-pingpong gate via the pingpong-detector agent before any dispatch fires. Use when invoked by the coliseum-orchestrator as Phase 3; when envelopes are already composed and the next step is parallel execution; or when the user asks to "fan out" or "dispatch in parallel." Produces a dispatch log capturing what was sent, when, and where returns land.
assignment-composition
Wraps each surfaced dimension as a self-contained 9-section autonomous-work-assignment envelope — scope, context, success criteria, allowed tools, return format, handoff — all the recipient subagent needs to execute without coming back. Use when invoked by coliseum-orchestrator as Phase 2; when dimensions are named and the next step is to make each independently dispatchable; or when the user asks "compose this as an assignment." The no-pingpong gate validates each envelope before dispatch.
workspace-autopsy-governance
Conducts a full automated autopsy of the current workspace directory to map files, identifies structural issues, proposes a restructuring plan (the signal), and establishes unified governance using templates. Use this skill when a user asks to map, restructure, reorganize, or apply new governance to an existing messy repository.
workshop-presentation-design
Design engaging workshops, conference talks, and educational presentations. Covers learning objectives, activity design, slide craft, and facilitation techniques. Triggers on workshop design, presentation prep, talk structure, or training session requests.
webhook-integration-patterns
Designs reliable webhook systems with proper delivery guarantees, retry logic, signature verification, and idempotent processing for event-driven integrations.